Reproducible Analyses

What is it and why should I care?

Daniela Palleschi

Humboldt-Universität zu Berlin

2023-04-12

Replication

“There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims”

– Ioannidis (2005)

  • replication refers to re-running a previous experiment with as few differences as possible
    • aim: determine whether the original results were robust and are replicable
    • if yes, great! the original findings are reliable
    • if no, hmm, maybe the original findings were false positives? or due to some other factor?
  • in recent years, researchers have tried to replicate classic studies in their field
    • but in many cases, they did not get the same effects the original study reported (and were famous for)
  • this began the replication crisis

An example from language research

  • Nieuwland et al. (2018): a direct EEG1 replication (versus conceptual replication)
  • a multi-lab replication of DeLong et al. (2005)’s impactful paper
    • DeLong et al. (2005): reported N400 effects elicted at unexpected nouns, but also on preceding determiners (English a/an) when it signalled an unexpected word,
      • e.g., The day was breezy so the boy went outside to fly…a kite/*an airplane
      • taken as evidence of pre-activation of phonological form, graded by cloze probability
    • Nieuwland et al. (2018): replicated N400 at noun, but not at adjective
      • i.e., failure to replicate a famous finding

Reproducibility

  • reproducibility refers to the ability to reproduce somebody’s analyses with their
    • data
    • and code
  • it is not something we do once, nor is it something that will get us published
    • but it’s important for open science and encourages transparency

Replication vs. Reproducibility

  • replication of a study
    • repeating an experiment
    • getting similar results
  • reproducibility of analyses
    • repeating analyses of the same data
    • getting the same results
  • e.g., when you submit a paper to a journal, they make ask for your data and code so reviewers can reproduce your analyses
    • requires data and code
  • if you have interesting findings, other researchers (or future you) may want to replicate your study to see if they can replicate your findings
    • (may require) stimuli, set-up and presentation information, participant demographics

Open Science: Why should I care?

  1. Science is cumulative
    • We should ensure we’re building on reliable, robust findings
    • i.e., it’s good scientific practice
  2. Because the field cares
    • replication/reproducibility are beginning to be foregrounded by e.g., journals/job advertisements
  3. Helps future you
    • pre-registration, reproducible analyses, clean and shareable data: all help future you

What can I do?

  • there’s a variety of open science practices that we can choose to implement
  • some suggestions from Kathawalla et al. (2021):

Level: Easy

  1. Journal Club
  2. Project workflow
  3. Pre-prints

Level: Medium

  1. Reproducible code
  2. Sharing data
  3. Transparent manuscripts
  4. Pre-registration

Level: Difficult

  1. Registered reports

How to do better science

  • don’t be afraid of making mistakes
    • (most) researchers aren’t statisticians or programmers
    • do the best you can, and be transparent
  • doing some of the steps is better than doing none

What will we learn here?

Design and Reporting

  • Preregistration/Registered Reports
  • Transparent writing

Analysis

  • Reproducible code
    • with open source software (R, RStudio, packages)
    • dynamic reports with Quarto/Rmarkdown
  • Project workflow
    • folder structure
      • how to sensibly set up your folders
    • contained environments
      • using RProjects and the here package
Image source: Kathawalla et al. (2021) (all rights reserved)

R is for Reproducibility

  • we will be working with R, RStudio, Quarto, and RProjects
    • R: a programming language for statistical computing and graphics
    • RStudio: an integrated development environment (IDE)
      • RStudio Desktop
      • RStudio Server
    • Quarto (similar to Rmarkdown): dynamic reports
      • combining text, code, and printed tables and figures
    • RProjects: a workflow tool
      • contains all files necessary for a project
      • works with relative file paths

Exercises

RStudio

  1. Open RStudio
    • locate the Environment, Files, and Console panes
    • File > New File > R script
    • write [your birth-month number]*[the your birth day] and hit Enter
    • write print("Hello World!")
    • write number <- 3*32; this will create an object/variable ‘number’
    • write string <- "Hello World!"; this will create an object/variable ‘string’
    • write number
    • write string
    • add comments describing each step using #
    • File > Save As

# multiply 5 by 7
5*7
[1] 35
# print some text
print("Hello World!")
[1] "Hello World!"
# save an object 'number' with 5*7
number <- 5*7
# save an object 'string' with text
string <- "Hello World!"
# print number
number
[1] 35
# print string
string
[1] "Hello World!"
# do math with objects
number+number
[1] 70
number*number
[1] 1225
number*2
[1] 70
month <- 5
day <- 7
month*day
[1] 35

Quarto1

  • R scripts are a great way to keep track of what you did
    • however, the output is not saved, and adding comments with # gets kind of chunky
    • enter: dynamic reports!
  • dynamic reports are those that combine text, code, and output
    • they are a great tool for communicating, collaborating, and documenting
    • they are also fantastic for note-taking
  • Rmarkdown vs. Quarto
    • both can combine text with code, outputting PDFs, Word Documents, html, or slides
    • main difference: Quarto has native support of a wider range of programming languages (e.g., Python and Julia)
  • Want to know more? Check out Hadley Wickham’s intro (Wickham et al., n.d.)

YAML

---
title: "My title"
author: "My name"
format: html
---
  • YAML is a human-readable programming language used to configure documents
  • formatting is important: but be sandwiched between --- and ---
  • in Quarto the output type must at least be given (with R: pdf, html, revealjs)

Headings and text

# This is a heading

This is text.

## This is a sub-heading

This is more text.
  • headings are indicated by #
    • the number of #’s indicates the heading level

Code snippets

# do some math
year <- 1989
dog <- "Lola"
  • sandwiched between markdown```{r} and `markdown
    • shortcut: Ctrl/Cmd+Alt+I

In-line code

I was born on `r month`/`r day`/`r year`. My dog's name is `r dog`.

I was born on 5/7/1989. My dog’s name is Lola.

  • code output that was run above text can be called in-line using `r `

Altogether

---
title: "My title"
author: "My name"
format: html
---

# This is a heading

This is text.

## This is a sub-heading

This is more text.

Add some code chunks.

```{r}
# do some math
year <- 1989
dog <- "Lola"
```

And use call objects for in-line code: I was born on `r month`/`r day`/`r year`. My dog's name is `r dog`.

Quarto Exercises

  1. Create a new Quarto document
    • File > New File > Quarto Document
    • Read the instructions
    • Practice running the chunks individually
    • render the document
    • verify that you can modify the code, re-run it, and see modified output
  1. Create one new Quarto document for each of the three built-in formats: HTML, PDF and Word.
    • Render each of the three documents
    • How do the outputs differ?
    • How do the inputs differ?1

Quarto cont’d

  • Choose a Quarto document:
    • give it a title, your name (author), and unclick ‘Use visual markdown editor’
  • Render
  • YAML:
title: "Eye-tracking during reading"
subtitle: "Lecture 2 notes"
author: "[YOUR NAME HERE]"
lang: en
date: `r Sys.Date()`

  • Render

  • you can now try writing your class notes in this document (if you’re brave)

References

DeLong, K. A., Urbach, T. P., & Kutas, M. (2005). Probabilistic word pre-activation during language comprehension inferred from electrical brain activity. Nature Neuroscience, 8(8), 1117–1121. https://doi.org/10.1038/nn1504
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Med, 2(8), 2–8. https://doi.org/10.1371/journal.pmed.0020124
Kathawalla, U.-K., Silverstein, P., & Syed, M. (2021). Easing Into Open Science: A Guide for Graduate Students and Their Advisors. Collabra: Psychology, 7(1), 18684. https://doi.org/10.1525/collabra.18684
Nieuwland, M. S., Politzer-Ahles, S., Heyselaar, E., Segaert, K., Darley, E., Kazanina, N., Von Grebmer Zu Wolfsthurn, S., Bartolozzi, F., Kogan, V., Ito, A., Mézière, D., Barr, D. J., Rousselet, G. A., Ferguson, H. J., Busch-Moreno, S., Fu, X., Tuomainen, J., Kulakova, E., Husband, E. M., … Huettig, F. (2018). Large-scale replication study reveals a limit on probabilistic prediction in language comprehension. eLife, 7, e33468. https://doi.org/10.7554/eLife.33468
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (n.d.). R for Data Science (2nd ed.). https://r4ds.hadley.nz/

Reproducibility